class_docstr: Sharding spec for a tensor, it contains info of the logical device mesh this tensor belong
  to, the entire shape of the tensor before sharded, and the sharding sequence looks like
  [R, R, S0, S1].
  
  Argument:
      device_mesh(DeviceMesh): A logical view of a physical mesh.
      entire_shape(torch.Size): The entire shape of tensor before sharded.
      dim_partition_dict(Dict[int, List[int]]， optional): The key is the dimension of tensor to be sharded,
          and the value of the key describe which logical axis will be sharded in that dimension.
      sharding_sequence(List[_DimSpec], optional): A straight view of ShardingSpec looks like [R, R, S0, S1].
code: def get_sharded_shape_per_device(self):
      sharded_shape = list(self.entire_shape)
      for dim, shard_list in self.dim_partition_dict.items():
          mesh_list = [
              self.device_mesh.shape[mesh_dim]
              for mesh_dim in shard_list
          ]
          shard_partitions = reduce(operator.mul, mesh_list, 1)
          assert (
              sharded_shape[dim] % shard_partitions == 0
          ), f"Cannot shard dimension {dim} into {shard_partitions} partitions."
          sharded_shape[dim] //= shard_partitions
      return torch.Size(sharded_shape)
  
imports: import operator
  from copy import deepcopy
  from functools import reduce
  import torch
  from colossalai.device.device_mesh import DeviceMesh
  from .utils import merge_same_dim_mesh_list
label_desc: Use image node_id 7 for calling the ShardingSpec obj's underlying member method code with example usage: obj.get_sharded_shape_per_device() and returns: torch